AI Model Serving with Kubeflow on Kubernetes using Multi-Tenancy and GPU Sharing

👋 Welcome, fellow DevOps engineers! In today’s fast-paced world of AI, deploying and managing AI models efficiently and securely is crucial. Many organizations are adopting Kubernetes to orchestrate their AI workloads. This post dives into a specific scenario: deploying a model serving application using Kubeflow on Kubernetes, focusing on multi-tenancy and GPU sharing to enhance security, performance, and resource utilization. We will explore practical deployment strategies, specific tools, and real-world implementations.

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Serving AI models at scale often requires significant compute resources, especially GPUs. In a multi-tenant environment, different teams or projects share the same Kubernetes cluster. This presents challenges related to security, resource isolation, and fair resource allocation. Kubeflow, a machine learning toolkit dedicated to Kubernetes, provides robust solutions for addressing these challenges. Using Kubeflow’s model serving component, combined with Kubernetes namespace isolation and GPU sharing technologies, allows for secure and efficient model deployment.

Let’s consider a scenario where two teams, Team Alpha and Team Beta, need to deploy their respective AI models on the same Kubernetes cluster. Team Alpha’s model requires high GPU resources for real-time inference, while Team Beta’s model is less resource-intensive and can tolerate lower GPU availability. To address this, we will leverage Kubernetes namespaces for isolation and NVIDIA’s Multi-Instance GPU (MIG) for GPU sharing.

First, we create separate namespaces for each team:

apiVersion: v1
kind: Namespace
metadata:
  name: team-alpha
---
apiVersion: v1
kind: Namespace
metadata:
  name: team-beta

Next, we configure ResourceQuotas and LimitRanges within each namespace to enforce resource constraints. This prevents one team from consuming all available resources, ensuring fair allocation. For example, we might allocate a higher GPU quota to Team Alpha due to their higher resource requirements:

# ResourceQuota for Team Alpha
apiVersion: v1
kind: ResourceQuota
metadata:
  name: gpu-quota
  namespace: team-alpha
spec:
  hard:
    nvidia.com/gpu: "2" # Allow up to 2 GPUs
---
# ResourceQuota for Team Beta
apiVersion: v1
kind: ResourceQuota
metadata:
  name: gpu-quota
  namespace: team-beta
spec:
  hard:
    nvidia.com/gpu: "1" # Allow up to 1 GPU

To enable GPU sharing, we’ll leverage NVIDIA’s MIG feature (available on A100 and newer GPUs). MIG allows a single physical GPU to be partitioned into multiple independent instances, each with its own dedicated memory and compute resources. We can configure the Kubernetes node to expose MIG devices as resources. This usually requires installing the NVIDIA device plugin and configuring the node’s MIG configuration.

For example, if we have an A100 GPU, we can partition it into seven 1g.5gb MIG instances. We then expose these as schedulable resources in Kubernetes. This allows different pods, even within the same namespace, to request specific MIG instances. The `nvidia.com/mig-1g.5gb` resource name is then used in pod specifications.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: alpha-model-serving
  namespace: team-alpha
spec:
  replicas: 1
  selector:
    matchLabels:
      app: alpha-model
  template:
    metadata:
      labels:
        app: alpha-model
    spec:
      containers:
      - name: model-server
        image: your-alpha-model-image:latest # Replace with your model image
        resources:
          limits:
            nvidia.com/mig-1g.5gb: 1 # Request one 1g.5gb MIG instance
        ports:
        - containerPort: 8080

Kubeflow provides various model serving options, including KFServing (now superseded by KServe) and Triton Inference Server. KServe integrates seamlessly with Kubernetes and provides features like auto-scaling, canary deployments, and request logging. Triton Inference Server is also a popular choice for maximizing inference throughput. Using KServe, the model deployment becomes more streamlined:

apiVersion: serving.kserve.io/v1alpha1
kind: InferenceService
metadata:
  name: alpha-model
  namespace: team-alpha
spec:
  predictor:
    containers:
    - image: your-alpha-model-image:latest # Replace with your model image
      name: predictor
      resources:
        limits:
          nvidia.com/mig-1g.5gb: 1

For enhanced security, consider using network policies to restrict traffic between namespaces. This prevents unauthorized access to models and data. Implement role-based access control (RBAC) to control who can create, modify, and delete resources within each namespace. Regularly audit logs and monitor resource utilization to identify potential security breaches or performance bottlenecks. Implement data encryption at rest and in transit to protect sensitive model data. Tools like HashiCorp Vault can be integrated to securely manage secrets and credentials required by the model serving application.

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Conclusion

Real-world implementations of this approach are seen across various industries. Financial institutions use it to securely deploy fraud detection models, while healthcare providers leverage it for medical image analysis. E-commerce companies use multi-tenancy and GPU sharing to serve personalized recommendation models to different customer segments efficiently. Companies such as NVIDIA themselves, as well as cloud providers like AWS, Google, and Azure, actively promote and provide services around Kubeflow and GPU sharing.

By adopting a multi-tenant architecture with Kubernetes namespaces, resource quotas, and GPU sharing technologies like NVIDIA MIG, organizations can achieve a secure, high-performance, and resilient AI model serving platform. This approach optimizes resource utilization, reduces costs, and accelerates the deployment of AI-powered applications. Remember to continuously monitor, adapt, and improve your deployment strategy to stay ahead of the curve in the ever-evolving world of AI and Kubernetes! 🚀